comfydv

A collection of workflow efficiency and quality-of-life nodes built out of necessity for personal ComfyUI use.

Node	What it does
Format String	Formats a string from a Python f-string or Jinja2 template. Detects variables in the template and automatically adds/removes input sockets.
Random Choice	Accepts any number of typed inputs and outputs one at random, with a configurable seed for reproducibility.
Circuit Breaker	Halts the current ComfyUI queue run gracefully without crashing the server. Wire the status toggle to a boolean condition to skip the rest of the queue when a condition isn't met.
Ollama Client	Configures a connection to an Ollama server (default: http://localhost:11434). Threads the host URL through the graph as an OLLAMA_CLIENT socket.
Ollama Model Selector	Fetches the live model list from Ollama and presents it as a dropdown. Outputs the selected model name.
Ollama Load Model	Loads a model into Ollama's memory using /api/generate with keep_alive=-1.
Ollama Unload Model	Evicts a model from Ollama's memory using /api/generate with keep_alive=0.
Ollama Chat Completion	Sends a prompt (and optional conversation history) to Ollama /api/chat. Response and history are shown inline in the node body and available as output sockets.
Ollama Option — *	Seven composable option nodes (Temperature, Seed, Max Tokens, Top P, Top K, Repeat Penalty, Extra Body) that merge into an OLLAMA_OPTIONS dict wired into Chat Completion.
Ollama Debug History	Serialises an OLLAMA_HISTORY list to a pretty-printed JSON string for inspection.
Ollama History Length	Returns the number of messages in an OLLAMA_HISTORY list as an integer.

Install

Via ComfyUI Manager (recommended): search for comfydv and click Install.

Manual:

cd /path/to/ComfyUI/custom_nodes
git clone https://github.com/darth-veitcher/comfydv.git

Restart ComfyUI. The nodes appear under the dv/ and dv/ollama categories in the node menu. Runtime dependencies (jinja2, aiohttp) are installed automatically via requirements.txt.

For Ollama nodes: install Ollama and pull at least one model (ollama pull qwen2.5:latest) before using the Ollama nodes.

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Format String

Formats text from a Python f-string or Jinja2 template. As you type the template, input sockets appear and disappear automatically — one per variable detected.

Python f-strings

Type {variable_name} and a socket appears. Wire it to any string output in your workflow.

Output	Content
formatted_string	The rendered result
saved_file_path	Path written to disk (if save_path is set)
<var> …	Pass-through of each input value, for easy chaining

Jinja2 templates

Switch template_type to Jinja2 to unlock filters (| upper, | int, …), conditionals ({% if %}…{% endif %}), and loops.

Variables detected in {{ }} expressions become input sockets exactly as in Simple mode. See the Jinja2 documentation for the full filter/test reference.

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Random Choice

Connect any number of inputs of the same type. Each run picks one at random. Set seed for reproducibility.

• Accepts any ComfyUI type (STRING, IMAGE, CONDITIONING, …)
• Add as many inputs as you like; unused slots are removed automatically when disconnected
• seed = 0 randomises on every run; any other value locks the selection

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Circuit Breaker

Stops the queue gracefully when a condition isn't met — no crash, no error, just a clean halt.

Wire an image (or any trigger) into trigger and a boolean into status. When status is false the node raises InterruptProcessingException, which tells ComfyUI to stop the current run cleanly. When status is true the image passes through unchanged.

Typical use: skip an expensive upscale step when a quality-check node says the draft is already good enough.

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Ollama

14 nodes for integrating a local Ollama LLM into your ComfyUI workflow. The host URL is configured once in Ollama Client and threaded through the graph — all downstream nodes receive it via the OLLAMA_CLIENT socket.

Ollama Client node

Configure the server address once; all downstream Ollama nodes inherit it automatically.

Model lifecycle (load and unload)

On memory-constrained machines and single-GPU setups, explicitly loading and unloading the model before and after inference is critical. Ollama Load Model pins the model into VRAM (keep_alive=-1); Ollama Unload Model evicts it immediately (keep_alive=0), freeing memory for image generation or other models.

The correct chain is Load → Chat → Unload, enforced through data dependencies:

1. Wire OllamaLoadModel.model_name → OllamaChatCompletion.model. This creates the data dependency that guarantees Load runs before Chat and passes the model name into the Chat node's plain-string model input.
2. Wire OllamaChatCompletion.model_name → OllamaUnloadModel.model. This guarantees Unload runs after Chat completes.
3. Optionally wire OllamaChatCompletion.response → OllamaUnloadModel.passthrough — Unload returns the response unchanged so the rest of your workflow can still consume it.

Minimal chat workflow

1. Ollama Client → set host (default http://localhost:11434)
2. Ollama Model Selector → pick a model from the live dropdown (or type/wire a model name directly into Chat Completion's model input)
3. Ollama Chat Completion → wire client + model + prompt; the response appears inline in the node body and is also available as an output socket

Wire multiple nodes together for a complete end-to-end workflow:

Option nodes

Chain any combination of Ollama Option — nodes before Chat Completion to override inference parameters:

Option node	Ollama param
Temperature	temperature
Seed	seed
Max Tokens	num_predict
Top P	top_p
Top K	top_k
Repeat Penalty	repeat_penalty
Extra Body	arbitrary JSON merged into options

Multi-turn conversations

OLLAMA_HISTORY flows out of Chat Completion as a list of {"role", "content"} dicts. Wire it back into the next Chat Completion for multi-turn conversations, or inspect it with Ollama Debug History / Ollama History Length.